By Suli E., Mayers D.
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36–37 (2000) 13. : Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6, 239–251 (2002) 14. : Artiﬁcial immune systems: A new computational approach. Springer, Heidelberg (2002) 15. : Engene: The processing and exploratory analysis of gene expression data. Bioinformatics, 657–658 (2003) 16. : An immune-evolutionary algorithm for multiple rearrangements of gene expression data. Genetic Programming and Evolvable Machines 5(2), 157–179 (2004) 17.
Confidence The conﬁdence μqs for each contig is deﬁned as a measurement of the quality of the contributing base pairs . A strong signal indicates a correct read or less chance of an experimental error. Every base involved in the contig has a quality score, and the entire sequence can be a mix of low and high quality bases. The conﬁdence of a contig is the aggregate quality score of its contributing bases. For simplicity, the sum of weighted average quality scores is the conﬁdence of the contig.
Current approaches use pairwise sequence alignment as a method and instead of obtaining the shortest superstring, the longest common substring is used. To obtain the common substrings of two sequences, we are required to consider all possible substrings of the given sequences. The substring with the longest overlap is known as the longest common sequence (LCS). Finding the LCS for all possible sequences is an NP-hard problem. Thus, a brute-force approach is not feasible. Dynamic programming solves problems by combining the solutions to subproblems to reduce the runtime of algorithms containing overlapping subproblems and optimal substructures .